In recent years, Convolutional Neural Networks (CNNs) have led to several improvements in computer vision.
Fully convolutional networks (FCN) [1]} achieved a significant improvement in the SS task in contrast to the traditional SS techniques [2]}.
However, (i) the low-resolution at the CNNs output and (ii) the loss of spatial precision of objects within the image are still the main problems that affect the segmentation results [3]}, [4]}, see Fig. REF .
We believe that these problems are not caused by a specific operation (e.g., down-sampling) but by a set of factors.
For instance, the absence of reconstruction and refinement methods, the excessive down-sampling, the gradient vanishing problem, or lack of a better extractor of feature maps.
